Chronic obstructive pulmonary disease (COPD) is a highly and increasingly prevalent disorder characterized by incomplete reversible airflow limitations. It is presently the third leading cause of death in the US, with over 13 million peopl suffering from the disease. Pulmonary function tests are used to identify global lung function impairment; however, determining the specific cause of decreased lung function is necessary in order to choose the best treatment strategy. We have recently developed the Parametric Response Map (PRM) as a CT-based imaging biomarker capable of diagnosing the two major phenotypes in COPD: functional small airways disease (fSAD) and emphysema. This diagnostic biomarker is a game-changer for radiological and pulmonary medicine practice, but in order for its impact to be fully realized, a commercial, FDA-approved diagnostic analysis and reporting software application must be developed. We propose to develop PRM as a CT-imaging biomarker for assessment and diagnosis of COPD phenotypes and for visualizing detailed spatial information related to disease location. The goals of this proposal will focus on development of a commercial PRM diagnostic software application, including its validation as a surrogate marker of patient health status, using images and patient information obtained from the NIH-sponsored clinical trial COPDGene. First, the PRM algorithm will be developed into a commercially viable software package. After validating the output of each individual component of the commercial PRM software against the in-house analysis of the inventors of PRM at the University of Michigan, the complete PRM analysis of 194 patients by the new software will be compared to previously published results to confirm that the commercial software gives accurate PRM results. Then the CT images from 500 patients will be analyzed to optimize the input parameters of the PRM analysis to ensure that the PRM results accurately predict COPD progression. Overall, it is anticipated that the successful outcome of this effort will significanty improve the diagnostic capability of CT imaging for COPD, leading to improved patient care and a reduction in cost to the healthcare system.